Stanford CS329A (Ep 13): LLM Reasoning — Denny Zhou, Chain-of-Thought (CoT), Self-Consistency, and Emergent Abilities
For decades, artificial intelligence researchers operated under a deeply entrenched skepticism regarding connectionist neural networks:
"A deep language model is merely a statistical next-token auto-completer. When tasked with multi-step arithmetic, formal logic, or intricate causal reasoning, it produces superficial hallucinations because statistical sequence modeling is fundamentally incapable of genuine logical deduction."
When presented with complex grade-school math problems, early frontier models immediately blurted out incorrect numbers, failing simple addition and subtraction across multi-hop word problems.
In this kickoff to Part 4: Frontiers (Lectures 13 through 17) of Stanford CS329A: Self-Improving AI Agents, we venture into the intellectual summit of artificial intelligence: LLM Reasoning Foundations. Guided by the pioneering research of Dr. Denny Zhou (Head of Reasoning at Google DeepMind), we investigate how the mysterious spark of cognitive reasoning ignites within large language models:
- Chain of Thought CoT Prompting (NeurIPS 2022): How decomposing tasks into natural language reasoning trajectories vaulted PaLM 540B's math accuracy from 18% to 57% without altering a single model weight.
- Self-Consistency Decoding LLM (ICLR 2023): Grounded in Tolstoy's Anna Karenina principle — "Valid reasoning paths converge toward singular truth, while errors disperse into chaos" — sampling diverse cognitive trajectories and voting out hallucinated noise.
- Emergent Cognitive Abilities AI (2022): Exploring physical phase transitions ("More is Different") and dissecting the historic debate: Is emergence a genuine physical breakthrough or a mathematical illusion created by non-linear metrics?